title: A bioinformatics workflow for variant peptide detection in shotgun proteomics.
abstract: Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics.
authors.affiliation: Department of Biomedical Informatics,Vanderbilt University School of Medicine, 2525 West End Ave, Suite 800, Nashville, TN 37232, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.forename: Zengliu
authors.initials: Z
authors.lastname: Su
authors.forename: Ze-Qiang
authors.initials: ZQ
authors.lastname: Ma
authors.forename: Robbert J C
authors.initials: RJ
authors.lastname: Slebos
authors.forename: Patrick
authors.initials: P
authors.lastname: Halvey
authors.forename: David L
authors.initials: DL
authors.lastname: Tabb
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Liebler
authors.forename: William
authors.initials: W
authors.lastname: Pao
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D002277:Carcinoma / Q000235:genetics / Q000378:metabolism; D045744:Cell Line, Tumor; D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism*; D019295:Computational Biology; D030562:Databases, Protein; D011905:Genes, ras; D006801:Humans; D050505:Mutant Proteins / Q000032:analysis*; D009154:Mutation; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D020543:Proteome / Q000032:analysis / Q000235:genetics; D040901:Proteomics / Q000379:methods*; D012811:Sigmoid Neoplasms / Q000235:genetics / Q000378:metabolism; D057188:Workflow
chemical_list: D050505:Mutant Proteins; D009363:Neoplasm Proteins; D020543:Proteome